Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "38" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 34 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 32 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459849 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.799373 | 0.702284 | 0.443676 | 2.059142 | 1.861285 | 3.961868 | 8.675206 | 2.898639 | 0.7582 | 0.7634 | 0.3595 | 3.479952 | 3.009874 |
| 2459848 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459847 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.379226 | 1.269572 | 0.930400 | -0.007562 | 0.550849 | 0.659661 | 5.753758 | 1.332855 | 0.7426 | 0.7042 | 0.4312 | 2.959447 | 2.752966 |
| 2459846 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.113183 | -0.407104 | 0.556215 | -0.817161 | 5.389748 | 2.329747 | 3.851333 | 2.705202 | 0.8477 | 0.6976 | 0.4782 | 3.063728 | 2.802405 |
| 2459845 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.329092 | 1.568385 | 2.889708 | -0.105851 | 1.966792 | 5.820669 | 5.632367 | 0.966648 | 0.7436 | 0.7662 | 0.3794 | 2.607421 | 1.758962 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 33.598483 | 26.827054 | 26.313814 | 21.052163 | 6.395518 | 13.537154 | 16.880338 | 13.344823 | 0.0217 | 0.0218 | 0.0003 | nan | nan |
| 2459843 | digital_ok | 100.00% | 0.66% | 0.66% | 0.00% | 100.00% | 0.00% | 0.606379 | 0.621394 | -0.850330 | 0.165027 | 2.371109 | 3.827660 | 5.178083 | 2.350239 | 0.7569 | 0.7635 | 0.3859 | 3.755380 | 3.570210 |
| 2459840 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 72.364929 | 53.992115 | 15.971979 | 13.783817 | 6.888480 | 6.288886 | 15.390992 | 17.117067 | 0.0195 | 0.0195 | 0.0004 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.403593 | -0.001784 | -0.618555 | -0.861529 | 3.844938 | 3.891906 | 5.154526 | 2.453812 | 0.7644 | 0.7115 | 0.4027 | 4.040147 | 3.918143 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0304 | 0.0325 | 0.0012 | nan | nan |
| 2459835 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.198331 | 0.792989 | 2.227135 | -0.540975 | 0.828671 | 3.448771 | 4.812982 | 5.908732 | 0.0299 | 0.0317 | 0.0013 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.094934 | 7.150544 | 7.289582 | 5.910833 | 4.202800 | 9.193622 | 17.801801 | 13.509176 | 0.0235 | 0.0245 | 0.0010 | nan | nan |
| 2459832 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.245414 | -1.000321 | -1.074770 | -1.048223 | 0.671174 | 1.725500 | 6.790422 | 2.467953 | 0.8137 | 0.5380 | 0.5848 | 3.404227 | 2.975303 |
| 2459831 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 19.144488 | 14.468216 | 41.231880 | 36.192899 | 2.758228 | 2.913445 | 14.857469 | 16.101935 | 0.0203 | 0.0211 | 0.0007 | nan | nan |
| 2459830 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.239417 | -0.733294 | -1.059449 | -0.970895 | -0.917035 | 1.482736 | 13.163688 | 4.296425 | 0.8123 | 0.5445 | 0.5693 | 4.756569 | 4.020355 |
| 2459829 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.227257 | 0.195695 | 0.083633 | -0.367059 | 2.939268 | 6.348236 | 13.930166 | 6.758382 | 0.7665 | 0.6697 | 0.4172 | 26.316628 | 20.846894 |
| 2459828 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.427979 | -0.386898 | -0.127629 | -0.171086 | 1.031998 | 0.374083 | 18.271422 | 6.967025 | 0.8091 | 0.5550 | 0.5499 | 9.227733 | 5.048105 |
| 2459827 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.168769 | 0.332276 | -0.357227 | -0.798324 | 1.257173 | 1.545288 | 3.375329 | 0.215773 | 0.7728 | 0.6766 | 0.4193 | 1.289280 | 1.099029 |
| 2459826 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.075708 | -0.099563 | -0.712038 | -0.642800 | 0.141242 | -0.177535 | 12.620605 | 5.202848 | 0.8053 | 0.5736 | 0.5259 | 5.092489 | 3.795976 |
| 2459825 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.502762 | -0.464763 | -0.792256 | -0.997484 | -0.409965 | -0.130643 | 1.266487 | -0.418722 | 0.8046 | 0.5798 | 0.5306 | 1.936104 | 1.557436 |
| 2459824 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.017268 | 0.452779 | -0.495476 | -0.602130 | 1.735420 | -0.659548 | 8.192355 | 2.225654 | 0.7442 | 0.7348 | 0.3734 | 5.743864 | 5.020216 |
| 2459823 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.809851 | 0.235825 | -0.747559 | -0.803592 | 0.595494 | -0.850315 | 15.113180 | 3.026386 | 0.7762 | 0.6428 | 0.4712 | 4.659817 | 3.748822 |
| 2459822 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.264367 | -0.182592 | -0.524840 | -0.929635 | -0.666462 | -0.085250 | 1.640274 | -0.080973 | 0.8040 | 0.6087 | 0.5121 | 1.956836 | 1.604652 |
| 2459821 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.879891 | -0.455605 | -0.455033 | -0.839233 | -0.018965 | -0.217891 | -0.362299 | -0.453632 | 0.8006 | 0.6290 | 0.5120 | 1.771450 | 1.530341 |
| 2459820 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.415607 | 0.148982 | -0.137384 | 0.085826 | 4.995233 | 19.340428 | 9.232270 | 3.371561 | 0.7885 | 0.7042 | 0.4115 | 4.020507 | 4.086784 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.823571 | -0.372517 | -0.343165 | -0.963659 | -0.452557 | -0.892756 | 2.016052 | 0.553102 | 0.8147 | 0.6738 | 0.5017 | 1.849881 | 1.685865 |
| 2459816 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.870867 | -0.205724 | -1.048194 | -1.147589 | 0.368523 | 2.739605 | 15.107424 | 3.377757 | 0.8477 | 0.6070 | 0.5925 | 3.680652 | 3.427036 |
| 2459815 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.721426 | -0.249628 | -0.928753 | -1.112368 | 1.078228 | 0.404074 | 11.868679 | 3.426192 | 0.8061 | 0.6817 | 0.5082 | 3.286512 | 2.950392 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.052520 | 0.816624 | -0.206922 | -0.407178 | 6.397065 | 9.583014 | 12.471527 | 4.725827 | 0.8087 | 0.7459 | 0.3798 | 11.091749 | 11.944633 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 8.675206 | 0.799373 | 0.702284 | 0.443676 | 2.059142 | 1.861285 | 3.961868 | 8.675206 | 2.898639 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 5.753758 | 1.269572 | 0.379226 | -0.007562 | 0.930400 | 0.659661 | 0.550849 | 1.332855 | 5.753758 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Variability | 5.389748 | 1.113183 | -0.407104 | 0.556215 | -0.817161 | 5.389748 | 2.329747 | 3.851333 | 2.705202 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Variability | 5.820669 | 1.568385 | 1.329092 | -0.105851 | 2.889708 | 5.820669 | 1.966792 | 0.966648 | 5.632367 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Shape | 33.598483 | 33.598483 | 26.827054 | 26.313814 | 21.052163 | 6.395518 | 13.537154 | 16.880338 | 13.344823 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 5.178083 | 0.621394 | 0.606379 | 0.165027 | -0.850330 | 3.827660 | 2.371109 | 2.350239 | 5.178083 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Shape | 72.364929 | 72.364929 | 53.992115 | 15.971979 | 13.783817 | 6.888480 | 6.288886 | 15.390992 | 17.117067 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 5.154526 | -0.001784 | 0.403593 | -0.861529 | -0.618555 | 3.891906 | 3.844938 | 2.453812 | 5.154526 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Discontinuties | 5.908732 | 0.792989 | -0.198331 | -0.540975 | 2.227135 | 3.448771 | 0.828671 | 5.908732 | 4.812982 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 17.801801 | 7.150544 | 9.094934 | 5.910833 | 7.289582 | 9.193622 | 4.202800 | 13.509176 | 17.801801 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 6.790422 | 0.245414 | -1.000321 | -1.074770 | -1.048223 | 0.671174 | 1.725500 | 6.790422 | 2.467953 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Power | 41.231880 | 19.144488 | 14.468216 | 41.231880 | 36.192899 | 2.758228 | 2.913445 | 14.857469 | 16.101935 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 13.163688 | 1.239417 | -0.733294 | -1.059449 | -0.970895 | -0.917035 | 1.482736 | 13.163688 | 4.296425 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 13.930166 | 0.195695 | 2.227257 | -0.367059 | 0.083633 | 6.348236 | 2.939268 | 6.758382 | 13.930166 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 18.271422 | -0.386898 | 1.427979 | -0.171086 | -0.127629 | 0.374083 | 1.031998 | 6.967025 | 18.271422 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 3.375329 | 1.168769 | 0.332276 | -0.357227 | -0.798324 | 1.257173 | 1.545288 | 3.375329 | 0.215773 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 12.620605 | -0.099563 | 1.075708 | -0.642800 | -0.712038 | -0.177535 | 0.141242 | 5.202848 | 12.620605 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 1.266487 | -0.464763 | 0.502762 | -0.997484 | -0.792256 | -0.130643 | -0.409965 | -0.418722 | 1.266487 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 8.192355 | 0.017268 | 0.452779 | -0.495476 | -0.602130 | 1.735420 | -0.659548 | 8.192355 | 2.225654 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 15.113180 | 0.235825 | 0.809851 | -0.803592 | -0.747559 | -0.850315 | 0.595494 | 3.026386 | 15.113180 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 1.640274 | 1.264367 | -0.182592 | -0.524840 | -0.929635 | -0.666462 | -0.085250 | 1.640274 | -0.080973 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Shape | 0.879891 | -0.455605 | 0.879891 | -0.839233 | -0.455033 | -0.217891 | -0.018965 | -0.453632 | -0.362299 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Variability | 19.340428 | 1.415607 | 0.148982 | -0.137384 | 0.085826 | 4.995233 | 19.340428 | 9.232270 | 3.371561 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 2.016052 | 0.823571 | -0.372517 | -0.343165 | -0.963659 | -0.452557 | -0.892756 | 2.016052 | 0.553102 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 15.107424 | -0.205724 | 0.870867 | -1.147589 | -1.048194 | 2.739605 | 0.368523 | 3.377757 | 15.107424 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 11.868679 | -0.249628 | 0.721426 | -1.112368 | -0.928753 | 0.404074 | 1.078228 | 3.426192 | 11.868679 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 12.471527 | 0.816624 | 3.052520 | -0.407178 | -0.206922 | 9.583014 | 6.397065 | 4.725827 | 12.471527 |